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Computer Science

D-Index
47
Citations
11459
World Ranking
6383
National Ranking
2849

Overview

Vladimir Pavlovic is affiliated with Rutgers, The State University of New Jersey in the United States. Their research primarily spans the fields of Computer Science and Engineering, with a focus on Computer Vision and Pattern Recognition, Artificial Intelligence, Automotive Engineering, Building and Construction, and Biomedical Engineering.

The main topics explored in their work include:

  • Multimodal Machine Learning Applications
  • Advanced Image and Video Retrieval Techniques
  • Domain Adaptation and Few-Shot Learning
  • Image Retrieval and Classification Techniques
  • Anomaly Detection Techniques and Applications
  • Advanced Neural Network Applications
  • Generative Adversarial Networks and Image Synthesis

Pavlovic has contributed significantly to various publication venues. Frequent venues of publication include:

  • arXiv (Cornell University)
  • Computers & Graphics
  • Proceedings of the AAAI Conference on Artificial Intelligence
  • 2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)
  • SSRN Electronic Journal

Some recent papers authored or coauthored by Pavlovic are:

  • MUSE-VAE: Multi-Scale VAE for Environment-Aware Long Term Trajectory Prediction, 2022, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
  • Unsupervised Multi-Target Domain Adaptation: An Information Theoretic Approach, 2020, IEEE Transactions on Image Processing
  • Heterogeneous Crowd Simulation Using Parametric Reinforcement Learning, 2021, IEEE Transactions on Visualization and Computer Graphics
  • Predicting Crowd Egress and Environment Relationships to Support Building Design Optimization, 2020, Computers & Graphics
  • CHEF: Cross-modal Hierarchical Embeddings for Food Domain Retrieval, 2021, Proceedings of the AAAI Conference on Artificial Intelligence

Their frequent coauthors include:

  • Mubbasir Kapadia
  • Sejong Yoon
  • Samuel S. Sohn
  • Ricardo Guerrero
  • Kalliopi Basioti

Best Publications

  • Visual interpretation of hand gestures for human-computer interaction: a review

    V.I. Pavlovic;R. Sharma;T.S. Huang

  • A New Baseline for Image Annotation

    Ameesh Makadia;Vladimir Pavlovic;Sanjiv Kumar

  • Face tracking and recognition with visual constraints in real-world videos

    Minyoung Kim;S. Kumar;V. Pavlovic;H. Rowley

  • Toward multimodal human-computer interface

    R. Sharma;V.I. Pavlovic;T.S. Huang

  • Learning Switching Linear Models of Human Motion

    Vladimir Pavlovic;James M. Rehg;John MacCormick

  • A dynamic Bayesian network approach to figure tracking using learned dynamic models

    V. Pavlovic;J.M. Rehg;Tat-Jen Cham;K.P. Murphy

  • Discovering clusters in motion time-series data

    J. Alon;S. Sclaroff;G. Kollios;V. Pavlovic

  • Baselines for Image Annotation

    Ameesh Makadia;Vladimir Pavlovic;Sanjiv Kumar

  • RankGene: identification of diagnostic genes based on expression data.

    Yang Su;T. M. Murali;Vladimir Pavlovic;Michael Schaffer

  • Context-Sensitive Dynamic Ordinal Regression for Intensity Estimation of Facial Action Units

    Ognjen Rudovic;Vladimir Pavlovic;Maja Pantic

  • Impact of dynamic model learning on classification of human motion

    V. Pavlovic;J.M. Rehg

  • Deep Structured Learning for Facial Action Unit Intensity Estimation

    Robert Walecki;Ognjen Rudovic;Vladimir Pavlovic;Bjoern Schuller

  • Time-series classification using mixed-state dynamic Bayesian networks

    V. Pavlovic;B.J. Frey;T.S. Huang

  • Dynamic bayesian networks for information fusion with applications to human-computer interfaces

    Thomas S. Huang;Vladimir Ivan Pavlovic

  • Multi-output Laplacian dynamic ordinal regression for facial expression recognition and intensity estimation

    Ognjen Rudovic;Vladimir Pavlovic;Maja Pantic

  • Speech-Driven 3D Facial Animation with Implicit Emotional Awareness: A Deep Learning Approach

    Unknown

  • Method for visual tracking using switching linear dynamic system models

    Vladimir Pavlović;James Matthew Rehg

  • Boosted Bayesian network classifiers

    Yushi Jing;Vladimir Pavlović;James M. Rehg

  • A graphical model framework for coupling MRFs and deformable models

    Rui Huang;V. Pavlovic;D.N. Metaxas

  • Gestural interface to a visual computing environment for molecular biologists

    V.I. Pavlovic;R. Sharma;T.S. Huang

  • Speech/gesture interface to a visual-computing environment

    R. Sharma;M. Zeller;V.I. Pavlovic;T.S. Huang

  • Boosting and structure learning in dynamic Bayesian networks for audio-visual speaker detection

    T. Choudhury;J.M. Rehg;V. Pavlovic;A. Pentland

Frequent Co-Authors

Thomas S. Huang
Thomas S. Huang University of Illinois at Urbana-Champaign
Maja Pantic
Maja Pantic Imperial College London
James M. Rehg
James M. Rehg University of Illinois at Urbana-Champaign
Dimitris N. Metaxas
Dimitris N. Metaxas Rutgers, The State University of New Jersey
Ashutosh Garg
Ashutosh Garg Google (United States)
Tat-Jen Cham
Tat-Jen Cham Nanyang Technological University
Kannan Ramchandran
Kannan Ramchandran University of California, Berkeley
Simon Kasif
Simon Kasif Boston University
Nicu Sebe
Nicu Sebe University of Trento

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